Adm Endeavors Inc Credit Rating

BOSTON (AI Credit Rating Terminal) Fri Jul 31 2020 19:49:03 GMT+0000 (Coordinated Universal Time) AI Credit Ratings today took the rating actions below:

Rating Action Overview


We downgraded Adm Endeavors Inc because of a weaker market position, higher risk, or more confidence-sensitive mix of business is only partially offset by any strengths, and this leads us to expect weaker revenue stability relative to peers, thus demonstrating modest incremental risk above what is captured in the anchor. We use econometric methods for period (n+30) simulate with Armstrong Oscillator Pearson Correlation. Reference code is: 3952. Beta DRL value REG 30 Rational Demand Factor LD 4345.9962. If, for example, a facility matured in 18 months, we could include the borrowing availability as a source of liquidity in year one, but exclude the amount in year two under the exceptional and strong descriptors (as well as include any drawn portions as debt maturities under uses of liquidity). This is because we do not assume an extension of bank lines--regardless of the company's perceived credit strength or issuer credit rating. For instance, whether the issuer credit rating on the company is speculative grade or investment grade, we do not assume bank lines will be extended beyond the current stated maturity. Credit Rating AI Process rely on primary sources of information: Sec Filings, Financial Statements, Credit Ratings, Semantic Signals. Take a look at Machine Learning section for Financial Deep Reinforcement Learning.Oscillators are used for generating credit risk signals by using the semantic and financial signals. The value of the oscillators indicate the strength of trend. Using the correlation matrices, the credit rating risk map for Adm Endeavors Inc as below:

Credit Ratings for Adm Endeavors Inc as of 31 Jul 2020


Credit Rating Short-Term Long-Term Senior
AI Rating Class*B3B1
Semantic Signals4671
Financial Signals4756
Risk Signals3343
Substantial Risks7041
Speculative Signals5782

*Machine Learning utilizes multiple learning algorithms to obtain better predictive powers. In our research, we utilize machine learning to combine the results from the Neural Network and Support Vector Machines.
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